Cost Uncertainty Research Articles

Designing Advance Market Commitments for New Vaccines

Advance market commitments (AMCs) provide a mechanism to stimulate investment by suppliers of products to low-income countries. In an AMC, donors commit to a fund from which a specified subsidy is paid per unit purchased by low-income countries until the fund is exhausted, strengthening suppliers’ incentives to invest in research, development, and capacity. Last decade saw the launch of a $1.5 billion pilot AMC to distribute pneumococcal vaccine to the developing world; in the current pandemic, variations on AMCs are being used to fund COVID-19 vaccines. This paper undertakes the first formal analysis of AMCs. We construct a model in which an altruistic donor negotiates on behalf of a low-income country with a vaccine supplier after the supplier has sunk investments. We use this model to explain the logic of an AMC—as a solution to a hold-up problem—and to analyze alternative design features under various economic conditions (cost uncertainty, supplier competition). A key finding is that optimal AMC design differs markedly depending on where the product is in its development cycle. This paper was accepted by Joshua Gans, business strategy.

Modelling the uncertainty of cost and time in highway projects

The construction of highway projects is characterised by cost overruns and time delays, due to the estimation approach and inappropriate analytical tools for predicting uncertainty. This study therefore developed a hybrid intelligent tool that models three sources of uncertainty in linear infrastructure projects: variability, correlation and disruptive events. The developed tool measures the effect of uncertainties on the cost and time of projects, by combining classical and intelligence prediction techniques. The variabilities were modelled using probability distributions; the copula technique modelled the correlations. The Markov processes simulated the occurrence of disruptive events. The adaptive neuro-fuzzy inference system was used to assess the size of the impact of disruptive events on the cost and time of activities. The total project cost and time were simulated by propagating the impact of the three sources of uncertainty in the Monte Carlo simulation environment. The developed uncertainty model was validated against the final cost and time of a highway project. The study found that the accumulated impact of the three sources of uncertainty significantly increased the construction cost and time of infrastructure projects. It concludes that the improvement in the accuracy of cost and time estimation of highway projects depends on a combination of classical and intelligent prediction techniques.

Risk-Calibrated conventional-renewable generation mix using master-slave portfolio approach guided by flexible investor preferencing

This paper proposes a master-slave approach to quantify, combine and balance energy-risk and cost-risk involved in a generation portfolio with renewable and fuel-based technologies. Subjective preferences of the investor on risk, cost and emission are traded-off using pareto-optimization and quantified using multi-criteria decision-making techniques. Temporal variations in renewable energy production led to ‘energy-risk’ (kWh), characterized by energy-return-risk Efficient Frontier (EF). The uncertainty in the energy production cost of the fuel-based sources (FBS) results in ‘cost-risk’ ($/kWh), represented by cost-risk EF. These efficient frontiers are combined using the concepts of Sharpe ratio and tangency portfolio. The master portfolio (MP) gives a percentage share for the total renewable and total conventional generation. The slave portfolio (SP) assigns internal weights within the renewable (solar and wind) and within the fuel-based (coal, natural gas, and oil) generations. The best solution is selected from the pareto-front by calibrating the investor preferences using Analytic Hierarchy Process (AHP) and then verified using Elimination and Choice Translating Reality (ELECTRE). It is understood that a completely customizable multi-criteria portfolio selection can be achieved by incorporating subjective views of the investors, eliminating one-sided energy portfolios.

Potential for Return on Investment in Rehabilitation-Oriented Blue Carbon Projects: Accounting Methodologies and Project Strategies

Opportunities to boost climate change mitigation and adaptation (CCMA) and sustainable conservation financing may lie in enhancing blue carbon sequestration, particularly in developing nations where coastal ecosystems are extensive and international carbon markets offer comparatively attractive payments for environmental stewardship. While blue carbon is receiving increased global attention, few credit-generating projects are operational, due to low credit-buyer incentives with uncertainty in creditable emissions reductions and high project costs. Little empirical guidance exists for practitioners to quantify return-on-investment (ROI) and viability of potential projects, particularly for rehabilitation where multiple implementation options exist with diverse associated costs. We map and model drivers of mangrove natural regeneration (NR) using remote sensing (high-resolution satellite imagery segmentation and time-series modeling), and subsequent carbon sequestration using field- and literature-derived data, across abandoned aquaculture ponds in the Philippines. Using project-specific cost data, we then assess ROI for a hypothetical rehabilitation-focused mangrove blue carbon project at a 9.68 ha abandoned pond over a 10-year timeframe, under varied rehabilitation scenarios [NR vs. assisted natural regeneration (ANR) with planting], potential emissions reduction accreditation methodologies, carbon prices and discount rates. NR was faster in lower-lying ponds with lower tidal exposure (greater pond dike retention). Forecasted carbon sequestration was 3.7- to 5.2-fold and areal “greenbelt” regeneration 2.5- to 3.4-fold greater in our case study under ANR than NR. Variability in modeled sequestration rates drove high uncertainty and credit deductions in NR strategies. ROI with biomass-only accreditation was low and negative under NR and ANR, respectively. ROI was greater under ANR with inclusion of biomass and autochthonous soil carbon; however, neither strategy was highly profitable at current voluntary market carbon prices. ANR was the only scenario that fulfilled coastal protection greenbelt potential, with full mangrove cover within 10 years. Our findings highlight the benefits of ANR and soils inclusion in rehabilitation-oriented blue carbon projects, to maximize carbon sequestration and greenbelt enhancement (thus enhance pricing with potential bundled credits), and minimize forecasting uncertainty and credit-buyers’ perceived risk. An ANR rehabilitation strategy in low-lying, sea-facing abandoned ponds with low biophysical intervention costs may represent large blue carbon CCMA opportunities in regions with high aquaculture abandonment.

Case study of an equivalent annual cost model for economic lifetime for construction vehicles under cost uncertainty

Whereas the practical importance of the Economic Lifetime (EL) is well-known, selecting the proper process has always been a dilemma. In this respect, classical methods dating back to one century ago are generally favored, but using them in a data-driven approach still has particular shortcomings. This paper aims to present a Life Cycle Cost (LCC) model determining the EL of a truck while fluctuation in historical data deepens through its lifespan. The equivalent annual cost of LCC is developed based on Operating and Maintenance (O&M) costs along with the resale value. The O&M cost was estimated deterministically and stochastically using regression analysis and Brownian-Motion-based simulation. The resale value was modeled by employing a genetic algorithm. The model capability was evaluated using real data of a seven cubic-meters truck hauling rock-fill materials in a dam construction project. The optimal EL was estimated on average 105 months in deterministic condition, while it was 88-145 months at the 70% confidence level using non-deterministic approach.

Broad Ranges of Investment Configurations for Renewable Power Systems, Robust to Cost Uncertainty and Near-Optimality

To achieve ambitious greenhouse gas emission reduction targets in time, the planning of future energy systems needs to accommodate societal preferences, e.g. low levels of acceptance for transmission expansion or onshore wind turbines, and must also acknowledge the inherent uncertainties of technology cost projections. To date, however, many capacity expansion models lean heavily towards only minimising system cost and only studying a few cost projections. Here, we address both criticisms in unison. While taking account of technology cost uncertainties, we apply methods from multi-objective optimisation to explore trade-offs in a fully renewable European electricity system between increasing system cost and extremising the use of individual technologies for generating, storing and transmitting electricity to build robust insights about what actions are viable within given cost ranges. We identify boundary conditions that must be met for cost-efficiency regardless of how cost developments will unfold; for instance, that some grid reinforcement and long-term storage alongside a significant amount of wind capacity appear essential. But, foremost, we reveal that near the cost-optimum a broad spectrum of regionally and technologically diverse options exists in any case, which allows policymakers to navigate around public acceptance issues. The analysis requires managing many computationally demanding scenario runs efficiently, for which we leverage multi-fidelity surrogate modelling techniques using sparse polynomial chaos expansions and low-discrepancy sampling.

Data-driven two-stage fuzzy random mixed integer optimization model for facility location problems under uncertain environment

<abstract><p>This paper studies the problem of facility location in a hybrid uncertain environment with both randomness and fuzziness. We establish a data-driven two-stage fuzzy random mixed integer optimization model, by considering the uncertainty of transportation cost and customer demand. Given the complexity of the model, this paper based on particle swarm optimization (PSO), beetle antenna search algorithm (BAS) and interior point algorithm, a hybrid intelligent algorithm (HIA) is proposed to solve two-stage fuzzy random mixed integer optimization model, yielding the optimal facility location and maximal expected return of supply chain simultaneously. Finally, taking the supply chain of medical mask in Shanghai as an example, the influence of uncertainty on the location of processing factory was studied. We compare the HIA with hybrid PSO and hybrid genetic algorithm (GA), to validate the proposed algorithm based on the computational time and the convergence rate.</p></abstract>

Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.

An adaptable robust optimization model for a dual-channel closed-loop supply chain considering cost and demand uncertainty

An adaptable robust optimization model for a dual-channel closed-loop supply chain considering cost and demand uncertainty

The effects of trade cost components and uncertainty of time delay on bilateral export growth

This paper measures the trends, specifically for exporters, in bilateral trade costs and more specifically for costs associated with policy barriers. The study further examines the effects of different trade cost components on bilateral export flows via the dynamic panel gravity model. Empirical evidence shows that trade costs generally have a declining trend globally, however, in the case of emerging countries, this rate of decline is very low. The costs associated with non-tariff barriers in emerging countries have a greater role in the continuation of higher trade costs, in comparison to tariff barriers' costs. Likewise, the two-steps system GMM model's results suggest that the infrastructure quality, transportation costs, volatility of exchange rate, and uncertainty in time (days) delay are major hurdles in the sustainable growth of exports and indeed have rendered the exports uncompetitive in the world market, those are also sturdily supported by the prediction of the theory.

Rituximab Use for the Treatment of Childhood Nephrotic Syndrome by Canadian Pediatric Nephrologists: A National Survey.

Background:There is known practice variation in the treatment of frequently relapsing, steroid-dependent, and steroid-resistant nephrotic syndrome in children. Rituximab is an emerging therapy for difficult-to-treat nephrotic syndrome; however, there are no clear treatment guidelines. We therefore hypothesized that a wide variety of approaches to this therapy exist.Objective:To evaluate when and how rituximab is used for the treatment of childhood nephrotic syndrome in Canada.Design and setting:An online survey was used.Participants:Canadian pediatric nephrologists.Methods:A cross-sectional survey was distributed across Canada through the Canadian Association of Pediatric Nephrologists (CAPN) to evaluate rituximab treatment practices.Results:Of a total of 20 responses, 19 (95%) use rituximab in the treatment of nephrotic syndrome, usually as a third or fourth agent. For the number of rituximab doses, the majority (68%) uses 2 doses each time they use it. Eighteen respondents (90%) measure B cells when using this medication, mostly monthly (50%) or every 3 months (39%). Respondents were administered additional doses of rituximab prophylactically (74%) or at first relapse (47%). Long-term drug safety and drug funding were identified as the main barriers to rituximab use.Limitations:This survey represents the practice styles of physicians in a single country, and there is a nonresponse bias of 63%. Also, associations were not calculated.Conclusions:Among Canadian pediatric nephrologists, rituximab use for nephrotic syndrome appears to be increasing, but significant practice variations remain, including approaches to B-cell monitoring. It is reserved mostly for second-line and third-line use due to cost, funding issues, and residual uncertainty regarding long-term safety. Understanding these critical areas of practice uncertainty is a first step to optimize treatment of nephrotic syndrome in children.

How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?

Many studies have demonstrated the feasibility of fully renewable power systems.Yet the future costs of key technologies are highly uncertain, and little is known about the robustness of a renewable power system to these uncertainties. To analyze it, we build 315 cost scenarios by varying the costs of key technologies and we model the optimal renewable power system for France, simultaneously optimizing investment and dispatch. We add to the literature by studying a consecutive 18-years weather period; by testing all combinations of technology costs rather than changing them one-at-a-time; and by calculating the regret from optimizing the energy mix on the basis of cost assumptions that do not materialize. Our results indicate that the cost of a 100% system is not that sensitive to uncertainty. Admittedly, the optimal energy mix is highly sensitive to cost assumptions: across our scenarios, the installed capacity in PV, onshore wind and power-to-gas varies by a factor of 5, batteries and offshore wind even more. However, in every scenario the total production and storage cost is similar to, or lower than the current cost. This indicates that renewable technologies will become by and large substitutable. Moreover, even if the energy mix is optimized based on cost assumptions which turn out to be wrong, the extra cost is low: 4% in average and less than 9% in 95% of the scenarios.

Committed versus contingent pricing under cost uncertainty

AbstractIn practice, managers often face a trade‐off of choosing between committed and contingent pricing under cost uncertainty because contingent pricing may increase the firm's profit but may decrease consumer surplus and market share. This article compares the two pricing strategies under cost uncertainty in terms of the firm's profit, consumer surplus, and market share. We find conditions under which committed pricing would lead to higher consumer surplus and market share than contingent pricing and conditions under vice versa. The results highlight the properties of consumers' valuation distribution in the trade‐offs of choosing between committed and contingent pricing, specifically the properties of the so‐called Mills' ratio which is defined as the reciprocal of hazard rate. According to the curvature of Mills' ratio, we define three types of distributions. We then categorize some commonly used continuous distributions into the three types which lead to different preferences in the trade‐offs. Finally, we fit two sets of household income data from China and the United States into income distributions as the proxies of consumers' valuation distributions in an economy to illustrate those trade‐offs embedded in the two pricing strategies.

Cost uncertainty in an oligopoly with endogenous entry

AbstractHow does cost uncertainty affect the welfare consequences of an oligopoly? To answer this question, we investigate a Cournot oligopoly in which firms produce a homogeneous commodity and market entry is feasible. Marginal costs are unknown ex ante, that is, prior to entering the market. They become public knowledge before output choices are made. We show that uncertainty induces additional entry in market equilibrium and also raises the socially optimal number of firms. Since the first change dominates, the excessive entry distortion is aggravated. This prediction is robust to various extensions of the analytical setup. Furthermore, the welfare loss due to oligopoly tends to increase with uncertainty.

Public Provision of Goods and Services under Cost Uncertainty: The Government’s Bureaucratic Organization

The digital transition is a challenge that developed countries are currently facing. The transition process is associated with different degrees of uncertainty, which are particularly relevant for changes that have to do with the provision of goods and services produced by public administrations. Our paper uses a partial equilibrium model to study the effects of uncertainty on the public provision of goods and services produced by bureaucratic agencies, including the incentive of the government to consolidate production. We assume that bureaucratic agencies may play either a cooperative game with each other and a non-cooperative game against the government (i.e., a consolidated bureaucracy) or a non-cooperative game with each other and against the government (i.e., competing bureaus). Both the government and the bureaus face tradeoffs between maximizing the electorate preferences and extracting some political and/or bureaucratic rents. We find that a cooperative (competitive) bureaucratic solution depends on the nature of the goods produced. We find that costs’ uncertainty affects the level of public production and the way the policymakers extract their rents.

A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach

Recently, cost-based feature selection has received significant attention due to its great ability to achieve promising prediction accuracy at a minimum feature acquisition cost. To further improve its predictive and economic performances, this research proposes a cost-effective 1-norm support vector machine with group feature selection as GFS-CESVM1. Its robust counterpart model, GFS-RCESVM1, is also introduced to address the cost uncertainty of features and feature groups because cost variation commonly exists in real-world problems. The proposed models are formulated as Mixed Integer Linear Programming (MILP). To efficiently solve the proposed SVM MILP models, we develop a Branch-Cut-and-Price (BCP) algorithm that considers only a limited number of variables and/or constraints, which thereby leads to rapid convergence to an optimal solution. Various experimental results on benchmark and synthetic datasets demonstrate that GFS-CESVM1 can achieve competitive outcomes by considering not only individual feature evaluation but also group structural information among features. The GFS-RCESVM1 can identify the subset of features that is immune to cost uncertainty and therefore provide feasible and optimal solutions. Furthermore, our BCP algorithm can dominantly outperform the ordinary BB algorithm for finding better objective value and integrality gap within a short period of time.

Carbon-neutral methanol synthesis as carbon dioxide utilization at different scales: Economic and environmental perspectives

Methanol is emerging as the carbon dioxide utilization technology and hydrogen carrier that can produce methanol using captured carbon dioxide and hydrogen. In this study, four cases were classified as methanol production according to which pathway was used to produce hydrogen: case 1 (steam methane reforming), case 2 (coal gasification), case 3 (water electrolysis), and case 4 (direct methanol production by carbon dioxide electrolysis). To figure out the best pathway in terms of economic and environmental perspectives, itemized cost estimation, sensitivity analysis, uncertainty analysis, and carbon footprint analysis were performed for four cases of methanol production with methanol production capacities of 1, 10, 20, and 50 ton d−1. From itemized cost estimation reflecting carbon footprint analysis results, respective unit methanol production costs of 3.66, 2.99, 3.69, and 0.55 $ kg−1 were obtained for methanol production capacity of 50 ton d−1. Compared to the unit methanol production cost (0.396 $ kg−1) from fossil fuel-based production, direct methanol production has become feasible than other cases. And, from sensitivity and uncertainty analysis, the cost of reactant and electricity were major economic parameters for cases 1–3 and case 4, respectively, and the possible unit methanol production cost ranges due to the cost fluctuations in future were investigated.

ANALYSIS OF UNCERTAINTY IN A CONSTRUCTION PROJECT

This article presents the analysis of uncertainty and effectiveness in cost to construction project. Theoretical part describes various definitions of uncertainty, risk both with there sources as calculation bias, information ambiguity and data inaccuracy in construction projects. Main analysis is connected with the project's appraisal phase and its stages were the detailed information is the crucial factor for correct decision- making procedures under uncertainty and risk. Consequently this article is presenting various types risk what are undistinguishable from uncertainty circumstances in construction. After the description of risk and uncertainty types list of solutions were prepared. Application of the suggested risk and uncertainty extenuating techniques in practice was shown as analysis of precision and bias dependence with available information at different stages of appraisal phase.It is emphasized on uncertainty and risk management problem in the construction's decision- making. Recently methods based on the utility theory, game theory, statistical distribution and probability, were improved and adjusted due to decision-making needs in construction. That is why the model with implemented multiple-criteria approaches is suggested both as a tool for dealing with the uncertainty and risk problems and to meet the needs of project managers in appraisal phase.

Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

Trading and Volatility in Dual Market: Theory and Evidence from Real Estate

This article investigates the informational role of trading volume and the impact of transaction costs on trading volume and the return dynamics in public and private markets, using real estate as an application. The model extends Kyle (1985) in a framework with two markets associated with asymmetric transaction costs across markets and over time. It derives equilibrium and predicts that at the market downtime, investors’ incentive to trade in the public market increases while that in the private market declines based on a return-cost trade-off, which is when transaction cost in the private market accelerates. Trading volume induces return volatility. The marginal effect of trading volume on price increases monotonically with uncertainty in asset values and transaction costs. An assessment using a simulation method and data on public and private real estate markets in the United States from 1996 to 2014 validates the model’s propositions. We offer two explanations for the increased real estate investment trust (REIT) return volatility during the financial crisis: increased trading volume in the REIT market and a larger marginal impact of volume on price during market downtime.